In terms of languages, the majority of studies on this topic deal with English [1]}, [2]}, [3]}, [4]} due to the the wide availability of language resources such as corpora and pre-trained models. In recent years, several studies have been published on identifying offensive content in other languages such as Arabic [5]}, Dutch [6]}, French [7]}, Greek [8]}, Italian [9]}, Portuguese [10]}, and Turkish [11]}. Most of these studies have created new datasets and resources for these languages opening avenues for multilingual models as those presented in ranasinghe-etal-2020-multilingual. However, all studies presented in this section focused on classifying full texts, as discussed in the Introduction. MUDES objective is to fill this gap and perform span level offensive language identification.
